RecSim: A Configurable Simulation Platform for Recommender Systems (1909.04847v2)

Abstract: We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users. RecSim allows the creation of new environments that reflect particular aspects of user behavior and item structure at a level of abstraction well-suited to pushing the limits of current reinforcement learning (RL) and RS techniques in sequential interactive recommendation problems. Environments can be easily configured that vary assumptions about: user preferences and item familiarity; user latent state and its dynamics; and choice models and other user response behavior. We outline how RecSim offers value to RL and RS researchers and practitioners, and how it can serve as a vehicle for academic-industrial collaboration.

• The paper presents RecSim as a configurable simulation platform that enhances exploration of reinforcement learning in recommender systems through diverse user and document models.
• It details a modular architecture that simulates sequential interactions using configurable latent user features, document characteristics, and advanced user choice models.
• Case studies on latent state bandits, slate RL decomposition, and advantage amplification demonstrate improved engagement and robust algorithm performance.

RecSim: A Configurable Simulation Platform for Recommender Systems

The paper "RecSim: A Configurable Simulation Platform for Recommender Systems" provides a detailed account of the development and utility of RecSim, a simulation platform designed to enhance research and practical applications in the domain of recommender systems (RSs). This platform facilitates the modeling of sequential interactions with users, pushing the boundaries of current reinforcement learning (RL) methodologies within the landscape of interactive recommendation systems.

RecSim allows for the creation and configuration of varied simulation environments. These environments can assume different user preferences, item familiarity, latent states, and choice models, offering a flexible arena for researchers and practitioners to experiment with novel RS algorithms. The configurability extends to user features, document characteristics, and user choice models, providing a diverse range of scenarios to paper user engagement tactics.

Architecture and Components

RecSim encompasses several components essential for simulating RS interactions:

• User Model: Configurable characteristics such as latent user features (e.g., interests or satisfaction) and observable demographics and behavioral traits.
• Document Model: Features include document quality and observable traits like topic and popularity, which can influence user choice.
• User Choice Model: Determines user responses based on document features, governing user selections and satisfaction metrics.

The interaction between these components is orchestrated by the RecSim simulator, which integrates with OpenAI Gym to facilitate the application of various RL algorithms to recommendation tasks.

Case Studies and Findings

The paper presents instructive case studies using RecSim that shed light on specific challenges and solutions in RS environments:

1. Latent State Bandits: This paper investigates exploration strategies in environments where user interests are latent and variable. The findings highlight the significance of employing exploratory algorithms like Upper Confidence Bound (UCB1) in scenarios with high topic affinity, leading to better click-through rates compared to conventional approaches.
2. Slate RL Decomposition: The SlateQ algorithm leverages a decomposition technique to efficiently manage combinatorial action spaces in RSs, breaking down slate Q-values into tractable components. This approach demonstrates improved user engagement and robustness to variations in user choice models.
3. Advantage Amplification: This research addresses the challenges posed by low signal-to-noise ratios and slowly evolving user states. Hierarchical temporal aggregation strategies are shown to effectively amplify advantage functions, facilitating improved policy learning in recommenders.

Implications and Future Directions

RecSim offers a robust framework for both academic researchers and industry practitioners to explore and optimize RS algorithms within controlled and varied settings. The insights derived from such simulations can inform the development of RSs that prioritize long-term user satisfaction and engagement.

Future iterations of RecSim may integrate concurrent user interactions and enhanced modeling techniques for simulating mixed-mode dialogs and user transitions across varied search and browsing contexts. Emerging efforts aim to fit stylized user models using actual usage logs, paving the way for more realistic simulations that align closely with real-world RS phenomena.

In summary, RecSim stands as a pivotal tool to drive forward RL research in complex recommender environments, facilitating the exploration of nuanced interaction dynamics and the pursuit of innovative recommendation strategies.